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Evaluating Confidence in Toxicity Assessments Based on Experimental Data and In Silico Predictions.

Candice JohnsonLennart T AngerRomualdo BenigniDavid BowerFrank BringezuKevin M CroftonMark T D CroninKevin P CrossMagdalena DettwilerMarkus FrericksFjodor MelnikovScott MillerDavid W RobertsDiana Suarez-RodriguezAlessandra RoncaglioniElena Lo PiparoRaymond R TiceCraig ZwicklGlenn J Myatt
Published in: Computational toxicology (Amsterdam, Netherlands) (2021)
Understanding the reliability and relevance of a toxicological assessment is important for gauging the overall confidence and communicating the degree of uncertainty related to it. The process involved in assessing reliability and relevance is well defined for experimental data. Similar criteria need to be established for in silico predictions, as they become increasingly more important to fill data gaps and need to be reasonably integrated as additional lines of evidence. Thus, in silico assessments could be communicated with greater confidence and in a more harmonized manner. The current work expands on previous definitions of reliability, relevance, and confidence and establishes a conceptional framework to apply those to in silico data. The approach is used in two case studies: 1) phthalic anhydride, where experimental data are readily available and 2) 4-hydroxy-3-propoxybenzaldehyde, a data poor case which relies predominantly on in silico methods, showing that reliability, relevance, and confidence of in silico assessments can be effectively communicated within Integrated approaches to testing and assessment (IATA).
Keyphrases
  • electronic health record
  • molecular docking
  • big data
  • oxidative stress
  • data analysis
  • machine learning
  • drug induced